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为了提高货物或人体放射性筛查的可靠性,提出了一种基于主成分分析和Mahalanobis距离的异常γ能谱识别方法。该方法首先对大量不含异常放射性的测量对象产生的正常γ能谱进行主成分分析,提取出其所有主成分,并按从大到小的顺序,选取前若干主成分构成子空间;将正常及待识别γ能谱在此子空间上投影,得到它们的Mahalanobis距离,通过比较这些距离的相对大小实现对异常γ能谱的识别。Monte Carlo模拟实验和实际测试实验表明,在子空间信息量占原始信息比例大于99%时该方法可靠有效。
In order to improve the reliability of radioactive screening of cargoes or human beings, an identification method based on principal component analysis and Mahalanobis distance is proposed. In this method, the principal component analysis of the normal γ spectrum generated by a large number of objects without abnormal radioactivity is carried out, and all the principal components are extracted. From the descending order, the principal components are selected to form the subspace; And the γ energy spectrum to be identified is projected on the subspace to obtain their Mahalanobis distances. By comparing the relative sizes of these distances, the recognition of the abnormal γ spectra can be realized. Monte Carlo simulation experiments and practical tests show that this method is reliable and effective when the proportion of original information in subspace is more than 99%.